System thinking is a crucial cognitive framework to enable individual pro-environmental behavioral changes. Indeed, a large body of literature has shown a significant and positive association between individuals' system thinking capacities and perceptions of the threat posed by climate change. However, individual behavioral changes play a limited role in addressing climate change compared to large organizations involved in a significantly larger share of economic activities. Do organizations exhibit system thinking capacities? Here, we conjecture that system thinking is a cognitive framework observable at an aggregated group level and, therefore, organizations, not just individuals, can exhibit characteristic levels of system thinking. We conceptualize a definition of organizational system thinking and develop an empirical method to estimate it using a large body of textual data from business organizations. Then, we show that system thinking organizations are more likely to lower emissions and align them with the pathways required to meet the climate targets set by the Paris Agreement. Finally, we discussed the theoretical and policy implication of our study. Overall, our results suggest that system thinking is a relevant organization-level cognitive framework that can help organizations align their emissions with global climate targets.
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http://dx.doi.org/10.1073/pnas.2309510120 | DOI Listing |
Alzheimers Dement
December 2024
National Ageing Research Institute, Melbourne, VIC, Australia.
Background: We have co-produced with carers of people with dementia (hereafter carers) a culturally tailored iSupport Virtual Assistant (VA), namely e-DiVA, to support English-, Bahasa- and Vietnamese-speaking carers in Australia, Indonesia, New Zealand and Vietnam. The presented research reports qualitative findings from the e-DiVA user-testing study.
Method: Family carers and healthcare professionals working in the field of dementia care were given the e-DiVA to use on their smartphone or handheld device for 1-2 weeks.
Alzheimers Dement
December 2024
Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, México, DF, Mexico.
Background: The World Health Organization forecasts a population of 2,000 million people over 60 years by the year 2050, with 7% of this population suffering from dementia. Making a constant clinical-technological evaluation of older adults allows early detection of the disease and provides a better quality of life for the patient. In this sense, the research and development of innovative technological systems for the early detection of the disease, its monitoring and management of the growing number of patients with cognitive diseases has increased in recent years, integrating data collection and its automatic processing based on geriatric metrics into these systems using artificial intelligence (AI) methods.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
National Ageing Research Institute, Melbourne, VIC, Australia.
Background: We have co-produced with carers of people with dementia (hereafter carers) a culturally tailored iSupport Virtual Assistant (VA), namely e-DiVA, to support English-, Bahasa- and Vietnamese-speaking carers in Australia, Indonesia, New Zealand and Vietnam. The presented research reports qualitative findings from the e-DiVA user-testing study.
Method: Family carers and healthcare professionals working in the field of dementia care were given the e-DiVA to use on their smartphone or handheld device for 1-2 weeks.
Philos Trans R Soc Lond B Biol Sci
January 2025
Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK.
Africa boasts high biodiversity while also being home to some of the largest and fastest-growing human populations. Although the current environmental footprint of Africa is low compared to other continents, the population of Africa is estimated at around 1.5 billion inhabitants, representing nearly 18% of the world's total population.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts.
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